Processing Signals from a Sensor Group

ABSTRACT

Embodiments disclosed herein for processing signals from sensor groups include processor(s) configured to perform operations comprising sampling, at a configurable signal sampling interval, a plurality of signal sample values via the one or more output signal traces from the plurality of remotely located sensors; storing, in a transmission array, an available portion of parameters extracted from a first timeslot array and from a second timeslot array, wherein the available portion of parameters is extracted using an algorithm configured to assign specific parameters to the transmission array, independent of the sampling; and transmitting, to an external communications network, an available portion of the parameters stored in the transmission array, at a transmission interval, wherein the transmission interval is independent of timeslot durations of the first timeslot array and the second timeslot array, and wherein the transmitting operation is performed independently of the storing operation of the available portion of parameters.

BACKGROUND 1. Field

The present disclosure relates generally to collection and processing ofoutput signals from sensors located across, for example, a widegeographical area.

2. Information

When it is desired to monitor conditions across an area, for example, agroup of sensors may be employed at dispersed locations within the area,wherein output signals from sensors may be received at a centralizedparameter collection facility. At a parameter collection facility,output signals from the group of sensors may be converted, such as froman analog format to digital format, for example, and transmitted acrossa network, such as the Internet, a local area network, or the like. Insome instances, a parameter collection facility may make use of a massstorage device, such as a hard disk, for example, which may facilitatethe storage of, for example, hundreds of megabytes of parameterscorresponding to sensor output signals. However, use of mass storagedevices, such as hard disks, for example, may be especially problematicin harsh outdoor environments subject to large daytime/nighttimetemperature swings, changes in humidity, and so forth. In suchinstances, providing a low-cost mass storage based parameter collectionsystem may be particularly challenging.

BRIEF DESCRIPTION OF DRAWINGS

Claimed subject matter is particularly pointed and/or distinctly claimedin the concluding portion of the specification. However, both as toorganization and/or method of operation, together with objects,features, and/or advantages thereof, claimed subject matter may beunderstood by reference to the following detailed description if readwith the accompanying drawings in which:

FIG. 1 is a schematic diagram of a sensor group coupled to a processingcore according to an embodiment;

FIG. 2 is a schematic diagram of a sensor and a function processoraccording to an embodiment;

FIG. 3 is a schematic diagram of a sensor coupled to two functionprocessors according to an embodiment;

FIG. 4 is a schematic diagram of a sensor group, function processors,and timeslot arrays according to an embodiment;

FIG. 5 is a schematic diagram of a sensor coupled to a “Last” functionprocessor according to an embodiment;

FIGS. 6A-6B are a flowchart for a method of parameter collection in anintermittently-connected Internet environment according to anembodiment; and

FIG. 7 is a schematic diagram of a processor, which might be employed tocollect parameters from sensors in an intermittently-connected Internetenvironment.

Reference is made in the following detailed description to accompanyingdrawings, which form a part hereof, wherein like numerals may designatelike parts throughout to indicate corresponding and/or analogouscomponents. It will be appreciated that components illustrated in thefigures have not necessarily been drawn to scale, such as for simplicityand/or clarity of illustration. For example, dimensions of somecomponents may be exaggerated relative to other components. Further, itis to be understood that other embodiments may be utilized. Furthermore,structural and/or other changes may be made without departing fromclaimed subject matter. It should also be noted that directions and/orreferences, for example, up, down, top, bottom, and so on, may be usedto facilitate discussion of drawings and/or are not intended to restrictapplication of claimed subject matter. Therefore, the following detaileddescription is not to be taken to limit claimed subject matter and/orequivalents.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth to provide a thorough understanding of claimed subject matter. Forpurposes of explanation, specific numbers, systems, and/orconfigurations are set forth, for example. However, it should beapparent to one skilled in the relevant art having benefit of thisdisclosure that claimed subject matter may be practiced without specificdetails. In other instances, well-known features may be omitted and/orsimplified so as not to obscure claimed subject matter. While certainfeatures have been illustrated and/or described herein, manymodifications, substitutions, changes, and/or equivalents may occur tothose skilled in the art. It is, therefore, to be understood thatappended claims are intended to cover any and all modifications and/orchanges as fall within claimed subject matter.

Reference throughout this specification to “one implementation,” “animplementation,” “one embodiment,” “an embodiment” and/or the like maymean that a particular feature, structure, and/or characteristicdescribed in connection with a particular implementation and/orembodiment may be included in at least one implementation and/orembodiment of claimed subject matter. Thus, appearances of such phrases,for example, in various places throughout this specification are notnecessarily intended to refer to the same implementation or to any oneparticular implementation described. Furthermore, it is to be understoodthat particular features, structures, and/or characteristics describedmay be combined in various ways in one or more implementations. Ingeneral, of course, these and other issues may vary with context.Therefore, particular context of description and/or usage may providehelpful guidance regarding inferences to be drawn.

Operations and/or processing, such as in association with networks, suchas communication networks, for example, may involve physicalmanipulations of physical quantities. Typically, although notnecessarily, these quantities may take the form of signals and/orstates, such as magnetic and/or electrical signals, for example, capableof, for example, being stored, transferred, combined, processed,compared, and/or otherwise manipulated. It has proven convenient, attimes, principally for reasons of common usage, to refer to thesesignals as bits, data, values, elements, symbols, characters, terms,numbers, numerals, and/or the like. It should be understood, however,that all of these and/or similar terms are to be associated withappropriate physical quantities and are intended to merely be convenientlabels.

In this context, the terms “coupled,” “connected,” and/or similar terms,may be used. It should be understood that these terms are not intendedas synonyms. Rather, “connected” may be used to indicate that two ormore elements and/or other components, for example, are in directphysical and/or electrical contact; while, “coupled” may mean that twoor more components are in direct physical, including electrical contact;however, “coupled” may also mean that two or more components are not indirect contact, but may nonetheless co-operate and/or interact. The term“coupled” may also be understood to mean indirectly connected, forexample, in an appropriate context.

The terms, “and,” “or,” “and/or,” and/or similar terms, as used herein,may include a variety of meanings that also are expected to depend atleast in part upon the particular context in which such terms are used.Typically, “or” if used to associate a list, such as A, B, or C, isintended to mean A, B, and C, here used in the inclusive sense, as wellas A, B, or C, here used in the exclusive sense. In addition, the term“one or more” and/or similar terms may be used to describe any feature,structure, and/or characteristic in the singular and/or may be used todescribe a plurality or some other combination of features, structuresand/or characteristics. In this context, the term “between” and/orsimilar terms are understood to include “among” if appropriate for theparticular usage.

The “Internet” refers to a decentralized global network of interoperablenetworks, including devices that are part of such interoperablenetworks. The Internet includes local area networks (LANs), wide areanetworks (WANs), wireless networks, and/or long-haul public networksthat, for example, may allow signal packets to be communicated betweenLANs. The term world wide web (WWW) and/or similar terms may also beused to refer to the Internet. Signal packets, also referred to assignal packet transmissions, may be communicated between nodes of anetwork, where a node may comprise one or more network devices, forexample. As an illustrative example, but without limitation, a node maycomprise one or more sites employing a local network address. Likewise,a device, such as a network device, may be associated with that node. Asignal packet may, for example, be communicated via a communicationchannel and/or a communication path comprising the Internet, from a sitevia an access node coupled to the Internet. Likewise, a signal packetmay be forwarded via network nodes to a target site coupled to a localnetwork, for example. A signal packet communicated via the Internet, forexample, may be routed via a path comprising one or more gateways,servers, etc. that may, for example, route a signal packet in accordancewith a target address and availability of a network path of networknodes to a target address.

A network communications protocol, may include, without limitation,HTTP, HTTPS, Websockets, MQTT, AMQP, DDS, COAP, and other protocols,which may refer to a set of signaling conventions for communicationsbetween and/or among devices in a network, typically network devices,but may include computing devices, as previously discussed. For example,devices that substantially comply with a protocol and/or that aresubstantially compatible with a protocol may be included in the set ofsignaling conventions for communications between and/or among devices ina network. In embodiments, a network protocol has several layers. Theselayers may be referred to here as a communication stack. Various typesof communications may occur across various layers. For example, as onemoves higher in a communication stack, additional functions may beavailable by transmitting communications that are compatible and/orcompliant with a particular network protocol at these higher layers. Anetwork may be very large, such as comprising thousands of nodes,millions of nodes, billions of nodes, or more, as examples. Likewise, asensor group may be large, such as comprising hundreds of sensors,millions of sensors, and so forth, virtually without limitation.

In conventional settings, which do not benefit embodiments of claimedsubject matter, collection and storage of signal samples from remotelylocated sensors at a high rate of sampling prior to transmission ofsignal sample values across a communications channel (e.g., theInternet, a wireless LAN, etc.) can become cost prohibitive. In someinstances, co-locating a computerized parameter collection device, whichmay include a mass-memory device, located proximate with a sensor group,for example, may involve virtually continuous write and deleteoperations, which may quickly bring about deterioration of the massmemory device. Consequently, a computerized parameter collection devicemay require frequent, time-consuming, and/or costly maintenance. In someinstances, such as a parameter collection device located at a radio basestation, on a mountaintop, or in a reservoir or lake, for example,maintenance of such devices may be particularly challenging. Thus, insuch environments, a premium may be placed on equipment, and/or methodsof operating equipment, that permit parameter collection at high rates,such as one or more samples per second, without utilizing mass memorydevices. In embodiments, parameter collection equipment may operate toanalyze and store a relatively small number of parameters prior totransmitting relevant parameters across a communications channel, suchas the Internet, for example. Such devices may be particularly useful inenvironments in which connectivity to a network, such as the Internet,may be poor and/or intermittent, for example.

In particular embodiments of claimed subject matter, a “sensor network”or a “sensor group” may be used interchangeably to represent a pluralityof sensors to perform a wide variety of measurement operations. In onepossible example, a sensor network or sensor group may operate in acommercial farming environment, which may employ hundreds of sensors,thousands of sensors, tens of thousands of sensors, or an even greaternumber of sensors. In one possible embodiment, sensors may be dispersedover a large commercial farm to provide periodic and/or occasionalmeasurements of soil moisture content, local relative humidity, and/ortemperature, for example. Signal sample values from sensors may betransmitted to a parameter collection device, which may employ aprocessing core, for example, to format sensor measurements fortransmission along an external communications channel.

A sensor may operate to convert physical measurements to an analog ordigital signal, for example, according to a “reading type” of anindividual sensor. In this context, a “reading type,” which may be usedinterchangeably with a “sensor reading type,” means a sensor toimplement measurement of a particular type of physical quantity. Forexample, a sensor may implement a reading type to measure displacement,velocity, acceleration, vibrational frequency, or any other physicalquantity related to translational and/or vibrational motion of anobject. A sensor may implement a reading type to measure a chemicalparameter such as salinity, pH, alkalinity, toxicity, or concentration,for example. A sensor may implement a reading type to measure moisturecontent, temperature, heat flux, or illumination, for example. A sensormay implement a reading type to measure electrical parameters, such asvoltage, current, power factor, phase angle, capacitance, inductance,frequency, wavelength, distortion, or noise content, for example. Itshould be noted that claimed subject matter is intended to embracesensor types to implement virtually any reading type, withoutlimitation, that corresponds to a measurement of a physical quantity bya sensor in any type of parameter monitoring environment.

In embodiments, measurements from sensors may be converted from ananalog format to a digital format, for example, and may be processed inaccordance with one or more function processors. Accordingly, in thiscontext a “function processor” as the term is used herein means ahardware-based, signal processing operation, which may bring aboutextraction of parameters from output signal traces transmitted by, orotherwise obtained from one or more sensors of a sensor network. Thus,for the example of a sensor network to implement a reading typecorresponding to measurement of moisture content of soil at a locationof a commercial farm, output signal traces of one or more soil moisturesensors may be processed, via a function processor, to obtain a maximumvalue over a given duration or “timeslot,” such as a one-minutetimeslot, a 90-second timeslot, a one-hour timeslot, a one-day timeslot,a one-week timeslot, or a timeslot of any other suitable and/or usefulduration. In embodiments, such timeslots may be implemented utilizing anarray, such as a two-dimensional array, of a volatile memory, forexample. Alternatively, or in addition, a function processor may operateto obtain a minimum value of soil moisture over a given timeslot which,again, may comprise one minute, 90 seconds, one hour, one day, one week,for example. It should be noted that timeslots may comprise any timeduration, such as one minute, one one-hour, or one week, as previouslymentioned, or may comprise other time duration, such as a fraction ofone second (e.g., 1/100 of one second, 1/10 of one second, ⅓ of onesecond, and so forth.) Additionally, a timeslot may comprise any type ofmulti-second duration, such as two-second durations, three-secondperiod, five-second durations, and so forth. Further, a timeslot maycomprise several-minute durations, such as two-minute durations,four-minute durations, or any other desired, useful, and/or convenientduration, and claimed subject matter is intended to embrace timeslotscomprising any duration.

In particular embodiments, during a particular timeslot, a functionprocessor may perform a sampling operation to obtain one or more signalsample values. Accordingly, a “signal sampling interval” may be usedinterchangeably with a “sampling interval” to mean a duration betweensuccessive samples obtained during a particular timeslot. Inembodiments, a timeslot duration and a sampling interval may beconfigured to be independent from one another. For example, aspreviously mentioned, a timeslot may comprise a one-minute duration,while a sampling interval may comprise a smaller period of time, such as30 seconds, one second, one-half second, and so forth. Accordingly, inone possible example, if a function processor operates to determine amaximum signal sample value over a one-minute timeslot for a parametersampled at half-second intervals, the function processor may select fromamong 120 signal sample values to extract a maximum signal sample value.In another possible example, a function processor operating to determinean average value over a five-minute timeslot for a parameter sampled athalf-second intervals may process 600 signal sample values (e.g., 5.0minute×60 seconds/minute×2.0 samples/second) to extract an averagesignal sample value over computed over the five-minute timeslot.

In particular embodiments, a processing core, may operate to performnumber of processing functions utilizing output signal traces from avariety of sensor reading types (e.g., sensors to implement measurementof humidity, temperature, voltage, and so forth). Responsive toprocessing, such as utilizing a processing core, parameters extractedfrom signal sample values may be transmitted or reported by way of aninterface to an external communications channel, such as the Internet, alocal area network, or the like. In one possible example, a humiditysensor, which may measure humidity using a one-minute sampling interval,may report a maximum humidity value over a one-hour timeslot. Inembodiments, if connectivity to a high-quality external communicationschannel is available, signal sample values may be reported to theexternal communications channel on a near real-time basis.

However, responsive to a communications channel, such as the Internet,being momentarily or occasionally unavailable, a processing core maystore results of function processing in a timeslot array, which may bemaintained in a volatile memory. In embodiments, while a communicationschannel remains unavailable or inaccessible, a processor core may storeparameters computed by one or more function processors corresponding toadditional timeslots, such as one-hour timeslots, for example.Additionally, while a communications channel remains unavailable, afunction processor may continue to sample output signals from sensors,such as humidity sensors, at one-minute sampling intervals, for example.In response to a communications channel being reestablished or otherwisemade available, contents of a timeslot array may be rapidly transmittedthrough a communications channel. Responsive to such transmission, atimeslot array may be emptied, which may provide capacity for storage ofparameters in timeslots processed at future times.

FIG. 1 is a schematic diagram of a plurality of sensors coupled to aprocessing core according to an embodiment 100. As previously describedherein, a sensor group, such as sensors 101A, 101B, 101C, . . . , 101Z,as shown in FIG. 1, may comprise sensors to implement a reading type tomeasure any number of physical quantities. Accordingly, sensors 101A,101B, 101C, . . . , 101Z may provide output signal traces responsive tomeasurement of physical quantities such as quantities related totranslational and/or vibrational motion of an object (e.g.,displacement, velocity, acceleration, vibrational frequency, and soforth), chemical properties of a substance (e.g., pH, salinity,temperature, water content and/or water vapor content, and so forth),illumination-related quantities (e.g., intensity, wavelength, and soforth), electrical parameters (e.g., voltage, current, power, powerfactor, phase angle, capacitance, inductance, signal strength, and soforth), and a variety of additional physical quantities virtuallywithout limitation, and claimed subject matter is not limited in thisrespect.

Sensors 101A, 101B, 101C, . . . , 101Z may be coupled to processing core120 utilizing sensor connector 110, which may operate to route outputsignal traces from sensors to function processors 131, 132, 133, . . . ,139. It should be noted that although processing core 120 refers tofunction processors 131-139, indicating that processing core 120comprises nine function processors, particular embodiments may comprisea lesser number of function processors, such as four functionprocessors, five function processors, and so forth, or may comprise agreater number of function processors, such as 15 function processors,100 function processors, and so forth. It should be noted that claimedsubject matter is intended to embrace use of any number of functionprocessors, virtually without limitation.

In particular embodiments, function processors, such as functionprocessors 131-139, may perform analysis to extract parameters fromoutput signal traces from sensors 101A, 101B, 101C, . . . , 101Z, forexample. Accordingly, in one possible example, function processor 131may implement maximum-value processing in which signal sample valuesfrom one or more of sensors 101A, 101B, 101C, . . . , 101Z are sampled,such as at sub-second sampling intervals, for determination of a maximumvalue over a timeslot. In a possible embodiment, a function processor131 may sample output signal traces from sensor 101A at 0.5-secondintervals, and may extract a maximum value, computed during a one-minuteduration, for storage into a timeslot of timeslot array 141A.Accordingly, in such an example, function processor 131 may operate toextract a maximum value from among 120 samples (2.0 samples/second times60.0 seconds) for insertion into a timeslot of timeslot array 141A.

In embodiments, a timeslot array may be configured according to adesired transmission frequency. Thus, for example, in some instances itmay be advantageous to transmit one or more parameters relativelyinfrequently, such as, for example, one-hour intervals, two-hourintervals, etc. Accordingly, a timeslot array may be configured so as tostore signal sample values accumulated over commensurate intervals(e.g., one-hour intervals, two-hour intervals, etc.). In otherinstances, it may be advantageous to transmit one or more parametersmore frequently, such as, for example, 30-second intervals, one-minuteintervals, etc. Accordingly, a timeslot array may be configured so as tostore signal sample values accumulated over shorter durations, such as30-second intervals, one-minute intervals, just for examples. It shouldbe noted, however, that claimed subject matter is intended to embracetimeslot arrays encompassing a wide variety of sampling intervals,virtually without limitation.

In particular embodiments, operating virtually simultaneously withprocessing of function processor 131, function processor 132 may alsoobtain output signal traces from, for example, sensor 101A. In onepossible embodiment, a function processor 132 may implement an averagingfunction, which may comprise extracting an average signal sample valueof an output signal trace from sensor 101A, for example. Accordingly, inone nonlimiting example, function processor 132 may utilize the sameoutput signal trace utilized by function processor 131, but may extractan entirely different parameter for storage into a timeslot of timeslotarray 142A. Thus, again, in a possible example, function processor 132may process the identical (or nearly identical) signals sampled byfunction processor 131. However, rather than compute a maximum value,may compute an average value utilizing the 120 signal sample valuescorresponding to the output signal trace of sensor 101A. In embodiments,function processor 132 may operate to sample an output signal trace fromsensor 101A at a rate other than a signal sample rate utilized byfunction processor 131, such as sampling at 0.25 second intervals, forexample, and claimed subject matter is not limited in this respect

Hence, it can be appreciated that function processors 131-139 mayoperate to extract a number of parameters utilizing output signal tracesfrom sensor 101A for storage within timeslots of timeslot arrays141A-149A. In embodiments, function processors 131, 132, 133, . . . ,139 may implement a wide variety of logical and/or mathematicalfunctions utilizing output signal traces from sensors, such as obtaininga maximum, a minimum, or an average of signal sample values, forexample, for storage within timeslots 141A, 142A, 143A, . . . , 149A.Other logical and/or mathematical functions implemented by functionprocessors 131-139 may include computing a trend of signal samplevalues, computing a cumulative value of signal sample values, computinga derivative (e.g., first derivative, second derivative, and so forth)utilizing signal sample values, computing and integration of signalsample values (e.g., area under a curve of plotted signal samplevalues), computing a statistical variance of signal sample values,computing a median of signal sample values, or any combination thereof,and claimed subject matter is not limited in this respect.

In particular embodiments, network interface 170 may monitorconnectivity with an external communications network, such as theInternet, an Ethernet, or any other suitable communications network,utilizing a wired connection or a wireless connection, or a combinationthereof. In certain embodiments, responsive to network interface 170indicating that a connection to an external communications network ispresent, or that a connection exists that meets or exceeds a thresholdquality metric, parameters from timeslot arrays 141A-149A may bearranged into transmission array 150. In accordance with a TCP/IPprotocol, for example, network interface 170 may periodically, oroccasionally, transmit at least a portion of the contents oftransmission array 150 to an external communications network. Responsiveto transmission of at least a portion of transmission array 150, memorylocations corresponding to timeslots of timeslot arrays 141A-149A may bereused.

In particular embodiments, if transmission array 150 has been populatedwith parameters from timeslot arrays 141A-149A, memory locations storingthe parameters may be marked as “committed” to indicate that thetimeslot may be reused. Accordingly, at least in some embodiments,criteria for reuse of a particular timeslot may be based, at least inpart, on storage of a parameter extracted from a timeslot and insertedinto a transmission array, rather than actual transmission of contentsof the transmission array to an external communications network.Additionally, in particular embodiments, a transmission array may remain“uncommitted” to indicate its non-reusability, until the contents of thetransmission array has been transmitted to an external communicationsnetwork. It should be recognized, however, that in certain embodiments,a transmission array need not be completely filled in order fortransmission along an external communications network to occur. Thus, incertain embodiments, transmission array 150 may represent atransactional unit to indicate to a harvesting algorithm, for example,which operates to assign parameters harvested from timeslot arrays141A-149A, for example, that a new group of parameters may be assignedto transmission array 150.

Accordingly, if network interface 170 indicates that a connection existswith an external communications network, or a connection meeting orexceeding a threshold of a communications quality parameter,transmission array 150, after successful transmission to an externalcommunications network, may be periodically or nearly continuouslyupdated with timeslot parameters from one or more of timeslot arrays141A-149A. Additionally, as contents of transmission array 150 isupdated with parameters extracted from individual timeslots of timeslotarrays 141A-149A, individual timeslots of timeslot arrays 141A-149A maybe reused to store parameters responsive to processing by functionprocessors 131-139. Thus, it can be appreciated that as a consequence offrequent transmission of contents of transmission array 150 to anexternal communications network, timeslot arrays 141A-149A may consumeonly a small amount of volatile or nonvolatile memory. Accordingly,particular embodiments may operate in a manner that reduces a need torealize timeslot arrays 141A-149A implementing mass memory storageelements, such as memory storage elements greater than 50 MB, just toname a possible example.

On occasion, a connection to an external communications network maydegrade to a level that does not permit reliable communications betweenprocessing core 120 and network elements external to processing core120. On other occasions, communications with an external communicationsnetwork may be lost entirely. Accordingly, in such instances,transmission array 150 may accumulate parameters which, upon restorationof a connection with an external communications network, may betransmitted to the external communications network. However, whilecommunications with an external communications network is unavailable,function processors 131-139 may continue to insert parameters intotimeslots of timeslot arrays 141A-149A. Thus, in a possible example, iftimeslot array 141A comprises 100 timeslots, wherein each timeslotstores a maximum of signal sample values from a one-minute interval,timeslot array 141A may store up to 100 minutes of parameters responsiveto processing by function processor 131. Additionally, whilecommunications with an external communications network is unavailable,or does not exceed a quality threshold, a function processor 131 maycontinue to sample output signal traces from sensor 101A at a rate thatis unaffected by the availability or quality of communications with anexternal communications network.

Thus, in particular embodiments, despite the occasional unavailabilityof communications with an external communications network, functionprocessors 131-139 may continue to sample output signal traces fromsensors 101A-101Z at a rate that is unaffected by availability ofcommunications with network elements external to processing core 120.Additionally, responsive to restoration of communications with anexternal communications network, transmission array 150 may be populatedwith parameters from timeslot arrays 141A-149A. In embodiments, suchpopulation of transmission array 150 with parameters from timeslotarrays 141A-149A may operate to provide usable timeslots, which may bepopulated with parameters responsive to processing of output signaltraces by function processors 131-139.

Thus, in particular embodiments, such as instances in which ahigh-quality high-capacity external communications network is available,and in which parameters are extracted from signal sample values on arelatively infrequent basis, a transmission array may be only partiallyfilled prior to transmission of contents of the transmission array alongthe external communications network. In other instances, in whichcapacity of an external communications network is limited (e.g.,low-quality and/or low-capacity) transmission arrays may be likely to befull prior to transmission of contents along the external communicationsnetwork.

FIG. 2 is a schematic diagram of a sensor and a function processoraccording to an embodiment 200. Accordingly, as previously mentionedherein, in a possible example, function processor 131 operates toextract a maximum value from an output signal trace generated by sensor101A over a particular duration of a timeslot. Accordingly, functionprocessor 131A may store, such as by utilizing an internal memory array,maximum values of an output signal traces, such as voltage, just to namea possible example, for storage into timeslot array 141A. Functionprocessor 131A may select and/or or extract a parameter representing amaximum voltage acquired over the timeslot t₀-t₁ according to therelation: V_(max)=Max{V(t₀), . . . , V(t_(m)), . . . , V(t₁)}, in whichV(t_(m)) represents a signal sampling interval during which theparameter V_(max) is obtained.

FIG. 3 is a schematic diagram of a sensor coupled to two functionprocessors according to an embodiment 300. In embodiment 300, sensor101A, which may provide an output signal trace representing a measuredvoltage, may be conveyed to both function processor 131A and 132A.Accordingly, as shown in FIG. 3, an output signal trace from sensor 101Amay be processed utilizing function processor 131A, which may determinea parameter representing a maximum voltage value achieved during thetimeslot t₀-t₁. An output signal trace from sensor 101A may beadditionally processed utilizing function processor 132A, which mayextract or compute a parameter representing an average voltage acquiredover the timeslot t₀-t₁ substantially in accordance with the relation:V_(avg)={V(t₀), . . . , V(t₁)}.

FIG. 4 is a schematic diagram of a sensor group, function processors,and timeslot arrays according to an embodiment 400. As shown in FIG. 4,sensors 101A-101Z may represent sensors to provide, for example, anoutput signal trace corresponding to measurements of physicalquantities, chemical parameters, and so forth. In the embodiment of FIG.4, sensors 101A-101Z are coupled to function processors 131-139, whichmay perform function processing utilizing output signal traces at signalsampling intervals. Accordingly, in one possible example, functionprocessor 131 may compute a maximum value of output signal traces fromsensors 101A-101Z, for example. Thus, timeslot array 141A may bedesignated to store maximum values of an output signal trace from sensor101A over a duration of one minute, for example. Likewise, timeslotarray 141B may be designated to obtain and store maximum values of anoutput signal traces from sensor 101B over a duration of one minute, forexample. In a similar manner, additional timeslot arrays, such astimeslot arrays 141C-141Z, for example, may be utilized to obtain andstore maximum values from corresponding sensors, such as sensors101C-101Z. Thus, as shown in FIG. 4, output signal traces from numeroussensors may be processed by function processor 131, and results of suchfunction processing may be stored into a corresponding timeslot array.Additionally, although the above example describes timeslot arrays141A-141Z as comprising a duration of one minute, for example, in otherembodiments, timeslot arrays 141A-141Z may representdifferently-configured durations. For example, in one possibleembodiment, timeslot array 141A may store values analyzed over aduration of one minute, and timeslot array 141B may store valuesanalyzed over a duration of two minutes, for example.

In the embodiment of FIG. 4, function processor 132 may obtain outputsignal traces from sensors 101A-101Z, which may bring about storage ofresults, such as an average value of signal samples of output signaltraces obtained over a signal sampling interval. In a possible example,output signal traces to be averaged may be sampled at 0.5 secondintervals over a one-minute duration. In a similar manner, functionprocessor 139 may acquire output signal traces from sensors 101A-101Z,which may bring about storage of computed results, such as a minimumvalue of signal samples of output signal traces, to be stored intotimeslots 149A, 149B, . . . , 149Z. It should be noted that althoughembodiments of claimed subject matter are intended to embrace any numberof function processors, any number of sensors, and any number oftimeslot arrays, virtually without limitation.

FIG. 5 is a schematic diagram of a sensor coupled to a “Last” functionprocessor according to embodiment 500. As previously mentioned herein,function processors, such as function processors 131-139, for example,may experience occasional unavailability of communications with anexternal communications network. However, responsive to reestablishmentof high-quality communications with an external communications network,one or more function processors may be capable of virtually continuouslyupdating a timeslot array with signal sample values from sensors, suchas sensors 101A-101Z. Thus, in the example of FIG. 5, function processor231A performs a “Last” function in which signal sample values at aplurality of sampling intervals, such as all sample intervals, forexample, are stored in timeslot array 141A. Accordingly, for the exampleof FIG. 5, an output signal sample value from sensor 101A, such asV_(Last(n-1)), which may be sampled at time t_(Last(n-1)) may be madeavailable to a transmission array, such as transmission array 150 ofFIG. 1, for example, for immediate transmission to an externalcommunications network utilizing, for example, network interface 170also of FIG. 1. It should be noted that although FIG. 5 indicates showsonly a single sensor, such as sensor 101A, coupled to function processor231A, embodiments of claimed subject matter may include a plurality ofsensors, such as sensors 101A-101Z of FIG. 1, for example, coupled tofunction processor 231A to permit storage of a “Last” signal samplevalue from some or all of sensors 101A-101Z.

Accordingly, in particular embodiments, if a connection to ahigh-quality external communications network is available, functionprocessors of a processing core, such as processing core 120, forexample, may operate to convey signal sample values from a plurality ofsensors, such as sensors 101A-101Z, to an external communicationsnetwork virtually in real time. In particular embodiments, suchreal-time or near-real-time signal sampling may permit processing core120 to present unprocessed parameters (e.g., “raw data”) to an externalcommunications network. Further, if a connection to a high qualityexternal communications network is interrupted, a processing core, suchas processing core 120, for example, may return to a function processingoperation, in which parameters computed by function processors, such asfunction processors 131-139 of FIG. 1, for example, are stored in atimeslot array, such as timeslot arrays 141-149 of FIG. 1.

FIG. 6A is a flowchart for a method of parameter collection in anintermittently-connected Internet environment according to an embodiment600. Example implementations, such as described with respect to FIG.6A-6B, may include blocks in addition to those shown and described,fewer blocks, or blocks occurring in an order different than may beidentified, or any combination thereof. Although the sensors coupled tothe processing core of FIG. 1 may be utilized to perform the method ofFIG. 6A, the method may be performed by a variety of equipment, andclaimed subject matter is not limited in this respect. The method ofFIG. 6A may begin at block 610, which may comprise obtaining a signalsample values from a sensor at a signal sampling interval. In aparticular embodiment, block 610 may occur at a sampling intervalsignificantly smaller than a timeslot, such as a timeslot as previouslydescribed herein. In one possible embodiment, signal sample values maybe obtained from a sensor, such as described at block 610, at a signalsampling interval of 0.5 seconds, during a timeslot of for example 1.0minutes. Accordingly, in one nonlimiting example, if the signal samplevalue obtained at block 610 is to be directed to determining a maximumvalue of a sensor output trace, 120 signal sample values may beevaluated to determine an appropriate maximum value. However, claimedsubject matter is intended to embrace obtaining signal sample values tobring about any type of function processing, such as determination of amaximum value, a minimum value, an average value, a trend of signalsample values, a summation of values, a derivative of a curve formedfrom discrete signal sample values, and so forth, virtually withoutlimitation.

The method may continue at block 615, which may comprise applyingprocessor functions to comparing one or more obtained signal samplevalues with one or more previously obtained signal sample values. In onepossible example not intended to limit the scope of claimed subjectmatter, in which a maximum of signal sample values corresponding to avoltage over a timeslot extending from t₀-t₁ is to be determined, a mostrecently obtained value may be compared with a previously obtained valueto determine which of the two obtained values represents a greatersignal sample value. At block 620, a signal sample value, such as amaximum signal sample value may be stored in a timeslot array. Block 625may comprise a decision block at which a determination is made as towhether a timeslot interval has expired. If the decision of block 625indicates that a timeslot representing a particular duration (e.g., aone-minute timeslot, a 90-second timeslot, a one-our timeslot, etc.) hasnot expired, additional signal samples may be obtained, and the methodmay return to block 610. If, however, the decision of block 625indicates that a timeslot duration has expired, a function processor,such as function processors 131-139, of FIG. 4 for example, may move tothe next timeslot in a timeslot array, such as timeslot array 141A ofFIG. 4, for example.

It should be noted, again, that although blocks 610-630 are described inthe context of an approach toward obtaining a maximum signal samplevalue, blocks 610-630 may be adapted, configured, or modified toaccommodate processing any type of signal sample values, and claimedsubject matter is not limited in this respect. Additionally, it shouldbe noted that block 610-630 may be performed for any number of sensors,which may number into the dozens, hundreds, thousands, virtually withoutlimitation.

FIG. 6B is a flowchart for a method of parameter collection in anintermittently-connected Internet environment according to an embodiment650. It should be noted that FIG. 6A and FIG. 6B represent twoalgorithms that are independent from one another, meaning that thefrequencies at which the two algorithms are triggered can be configuredindependently. For example, the method of embodiment 650 may betriggered at a frequency different from a frequency at which embodiment600 is triggered. The correspondence between embodiment 600 andembodiment 650 allows a strong decoupling between sensor samplingfrequency and network transmission frequency.

At block 660, function processors of a processor core, such as processorcore 120 of FIG. 1, for example, may operate to populate or filltimeslots of a timeslot array with outcomes of processing of one or morefunction processors, such as described with respect to FIG. 6A. Block660 may additionally comprise collecting available timeslots, such asone or more portions timeslot arrays 141A-149Z of FIG. 4 into atransmission array, such as transmission array 150 of FIG. 1.Accordingly, block 660 may comprise storage of entire timeslot arrays,such as timeslot arrays that may store outcomes of particular functionprocessors, or may comprise storage of portions of a plurality oftimeslots, and claimed subject matter is not limited in this respect.

At block 665, a determination may be made as to whether a timeslot arrayhas been at least partially filled. If a decision indicates that atransmission array, such as transmission array 150 of FIG. 1, is empty,the operation of block 660 may be performed further so as to populate atransmission array with one or more values extracted from acorresponding number of timeslots. If the decision of block 665indicates that a transmission array has been at least partially filled,the method may proceed to block 670, in which a processor core, such asprocessor core 120 of FIG. 1, for example, may perform a connectivitytest to determine if a high-quality connection to an externalcommunications network is present. If the decision of block 675indicates that a quality metric of the external communications networkexceeds a threshold, contents of a transmission array (e.g., filled orpartially-filled) may be transmitted through the external communicationsnetwork. In embodiments, a quality metric may relate to a speed at whichparameters may be uploaded to the external communications network, aspeed at which parameters may be downloaded from the communicationsnetwork, a packet loss rate, and/or a measure of packet latency, just toname a few performance metrics, and claimed subject matter is notlimited in this respect.

As described above, in particular embodiments, a transmission array neednot be completely filled with parameters from a timeslot array in orderfor contents of the transmission array to be transmitted to an externalcommunications network. Accordingly, if a high-quality connection to anexternal communications network is present, parameters extracted from atimeslot array may be transmitted to the external communications networkvirtually immediately after storage in the transmission array.

If the decision of block 675 indicates that a high-quality connection toan external communications network is not present, block 670 may beperformed a second time, perhaps after a suitable period of time haselapsed (e.g., 100 milliseconds, ½ second, one second, 30.0 seconds, orother appropriate duration) connectivity to an external communicationsnetwork may be rechecked. Accordingly, blocks 670-675 may represent aloop, in which connectivity to an external communications network may bechecked and rechecked until such time as connectivity to the externalcommunications network is restored. If connectivity to an externalcommunications network has been restored, block 680 may be performed,which may comprise transmitting of contents of the transmission arraythrough the external communications network. Responsive to transmissionof signal sample values stored in the transmission array, block 660 maybe performed, which may comprise function processors of a processor corecollecting available timeslots, such as one or more portions oftimeslots 141A-149Z of FIG. 4.

In particular embodiments, block 680 may also represent a loop in whichportions of the contents of the transmission array are arranged into amessage structure, such as for transmission over the Internet, forexample. After a first portion of the contents of the transmission arrayare transmitted, a second portion may be arranged into a messagestructure for transmission, and transmitted, which may be followed byadditional portions of a transmission array until the entire contents ofthe transmission array has been conveyed to an external communicationsnetwork.

In particular embodiments, if connectivity to an external communicationsnetwork is available for greater than threshold time periods, such astime periods greater than 10.0 minutes, 15.0 minutes, 30.0 minutes, justto name a few nonlimiting examples, a function processor performing the“Last” function as described with respect to FIG. 5 may be particularlyadvantageous. In certain embodiments, if connectivity is relativelyreliable, a “Last” function processor may permit signal sample values tobe conveyed to an external communications network virtually in realtime. In particular embodiments, if connectivity is relatively reliable,operation of one or more function processors, such as functionprocessors 131-139, for example, may be paused in favor of a functionprocessor executing a “Last” function, which may permit functionprocessing of signal sample values external to a processing core, suchas processing core 120.

For purposes of illustration, FIG. 7 is a schematic diagram 700 of acomputing apparatus that may be employed for extracting parameters fromsensor output signal traces according to an embodiment. A computingapparatus, such as that embodied in FIG. 7 may comprise computing device710 that may be employed to perform operations such as, for example,described herein. Computing device 710 may interface with externalcommunications network 715 which may comprise features of a cellularcommunication network, an Ethernet, or any other wired or wirelesscommunications network, such as the Internet, utilizing any number ofprotocols such as HTTP, HTTPS, Websockets, MQTT, AMQP, DDS, COAP, and soforth, and claimed subject matter is not limited in this respect.

Communications interface 720, processor 750, and memory 770, maycommunicate by way of communication bus 740, for example. In FIG. 7,computing device 710 may store various forms of computer-implementableinstructions, by way of sensor interface 730, for example, to obtainoutput signal traces from sensors 101A-101Z. Although a computingdevice, such as the computing device embodied in FIG. 7 shows theabove-identified components, claimed subject matter is not limited tocomputing device having only these components as other implementationsmay include alternative arrangements that may comprise additionalcomponents, fewer components, or components that function differentlywhile achieving similar results. Rather, examples are provided merely asillustrations. It is not intended that claimed subject matter be limitedin scope to illustrative examples.

Processor 750 may be representative of one or more circuits, such asdigital circuits, to perform at least a portion of a computing procedureand/or process. By way of example but not limitation, processor 750 maycomprise one or more processors, such as controllers, microprocessors,microcontrollers, application specific integrated circuits, digitalsignal processors, programmable logic devices, field programmable gatearrays, and the like, or any combination thereof. In implementations,processor 750 may perform signal processing to manipulate signals and/orstates and/or to construct signals and/or states, for example.

Memory 770 may be representative of any memory storage technology.Memory 770 may comprise, for example, random access memory, read onlymemory, or one or more data storage devices and/or systems, such as, forexample, a solid-state memory drive, flash memory, just to name a fewexamples. It should be noted that, at least in particular embodiments,mass memory is may be utilized only for storage of executable processinginstructions (read-only), while the actual processing of signal samplevalues occurs in memory. This is to say that at least certainembodiments of claimed subject matter mass do not utilize mass memoryand memory 770 may be utilized to store a program, as an example. Memory770 may also comprise a memory controller for accessingcomputer-readable medium 780, which may process output signal traces forstorage into one or more timeslot arrays. Under direction of processor750, memory, such as cells storing physical states, representing, forexample, a program, may be executed by processor 750 and generatedsignals may be transmitted via the Internet, for example. Processor 750may also receive digitally encoded signals from computing device 710.External communications network 715 may comprise one or morecommunication links, processes, and/or resources to support exchangingcommunication signals between a client computing device and server,which may, for example, comprise one or more servers (not shown).

Thus, in embodiments, a method and apparatus may be implemented todecouple the frequency of collection of signal sample values from asensor group from the frequency of transmission of parameters across anexternal communications network. To bring about these features, a highlyefficient use of in-memory resources utilizing a timeslots approachreduces a need for mass memory, such as memories exceeding, 50 Mb, forexample.

The term “processing core,” as used herein, refers to a system and/or adevice, such as a computing device, that includes a capability toprocess and/or store data in the form of signals and/or states. Thus, acomputing device, in this context, may comprise hardware, software,firmware, or any combination thereof (other than software per se).Computing device 710, as depicted in FIG. 7, is merely one such example,and claimed subject matter is not limited to this particular example.For one or more embodiments, a computing device may comprise any of awide range of digital electronic devices, including, but not limited to,personal desktop or notebook computers, high-definition televisions,digital versatile disc (DVD) players and/or recorders, game consoles,satellite television receivers, cellular telephones, personal digitalassistants, mobile audio and/or video playback and/or recording devices,or any combination of the above. Further, unless specifically statedotherwise, a process as described herein, with reference to flowdiagrams and/or otherwise, may also be executed and/or affected, inwhole or in part, by a computing device.

Memory 770 may store cookies relating to one or more users and may alsocomprise a computer-readable medium that may carry and/or makeaccessible content, code and/or instructions, for example, executable byprocessor 750 or some other controller or processor capable of executinginstructions, for example. A user may make use of an input device and/oran output device, such as a computer mouse, stylus, track ball,keyboard, and/or any other device capable of receiving an input from auser.

Regarding aspects related to a communications and/or computing network,an external communications network may couple to sensors of a sensorgroup by way of a network. A wireless network may employ stand-alonead-hoc networks, mesh networks, Wireless LAN (WLAN) networks, cellularnetworks, or the like. A wireless network may further include a systemof terminals, gateways, routers, or the like coupled by wireless radiolinks, and/or the like, which may move freely, randomly or organizethemselves arbitrarily, such that network topology may change, at timeseven rapidly. Wireless network may further employ a plurality of networkaccess technologies, including Long Term Evolution (LTE), WLAN, WirelessRouter (WR) mesh, or 2nd, 3rd, or 4th generation (2G, 3G, or 4G)cellular technology, and/or other technologies, or the like. Networkaccess technologies may enable wide area coverage for devices, such asclient devices with varying degrees of mobility, for example.

A network may enable radio frequency and/or wireless type communicationsvia a network access technology, such as Global System for Mobilecommunication (GSM), Universal Mobile Telecommunications System (UMTS),General Packet Radio Services (GPRS), Enhanced Data GSM Environment(EDGE), 3GPP Long Term Evolution (LTE), LTE Advanced, Wideband CodeDivision Multiple Access (WCDMA), Bluetooth, 802.11b/g/n, or other, orthe like. A wireless network may include virtually any type of nowknown, or to be developed, wireless communication mechanism by whichsignals may be communicated between devices, such as a client device ora computing device, between or within a network, or the like.

Communications between a computing device and a wireless network may bein accordance with known and/or to be developed cellular telephonecommunication network protocols including, for example, global systemfor mobile communications (GSM), enhanced data rate for GSM evolution(EDGE), and worldwide interoperability for microwave access (WiMAX). Acomputing device may also have a subscriber identity module (SIM) card,which, for example, may comprise a detachable smart card that storessubscription information of a user, and may also store a contact list ofthe user. A user may own the computing device and/or may otherwise beits primary user, for example. A computing device may be assigned anaddress by a wireless telephony network operator, a wired telephonynetwork operator, and/or an Internet Service Provider (ISP). Forexample, an address may comprise a domestic and/or internationaltelephone number, an Internet Protocol (IP) address, and/or one or moreother identifiers. In other embodiments, a communication network may beembodied as a wired network, wireless network, or combination thereof.

A computing and/or network device may vary in terms of capabilitiesand/or features. Claimed subject matter is intended to cover a widerange of potential variations. For example, a network device may includea numeric keypad and/or other display of limited functionality, such asa monochrome liquid crystal display (LCD) for displaying text. Incontrast, however, as another example, a web-enabled computing devicemay include a physical and/or a virtual keyboard, mass storage, and/or adisplay with a higher degree of functionality, such as a touch-sensitivecolor 2D or 3D display, for example.

A computing device may include and/or may execute a variety of nowknown, and/or to be developed operating systems, or derivatives and/orversions, including personal computer operating systems, such as aWindows, iOS or Linux, or a mobile operating system, such as iOS,Android, or Windows Mobile, or the like. A computing device may includeand/or may execute a variety of possible applications, such as a clientsoftware application enabling communication with other devices, such ascommunicating one or more messages, such as via email, short messageservice (SMS), or multimedia message service (MMS), including via anetwork, such as a social network including, but not limited to,Facebook, LinkedIn, Twitter, Flickr, or Google+, to provide only a fewexamples. A computing device may also include and/or execute a softwareapplication to communicate content, such as, for example, textualcontent, multimedia content, or the like. A computing device may alsoinclude and/or execute a software application to perform a variety ofpossible tasks, such as browsing, searching, playing various forms ofcontent, including locally stored or streamed video, or games such as,but not limited to, fantasy sports leagues. The foregoing is providedmerely to illustrate that claimed subject matter is intended to includea wide range of possible features and/or capabilities.

A network including a computing device, for example, may also beextended to another device communicating as part of another network,such as via a virtual private network (VPN). To support a VPN,transmissions may be forwarded to the VPN device. For example, asoftware tunnel may be created. Tunneled traffic may, or may not beencrypted, and a tunneling protocol may be substantially complaint withand/or substantially compatible with any past, present or futureversions of any of the following protocols: IPSec, Transport LayerSecurity, Datagram Transport Layer Security, Microsoft Point-to-PointEncryption, Microsoft's Secure Socket Tunneling Protocol, MultipathVirtual Private Network, Secure Shell VPN, and/or another existingprotocol, and/or another protocol that may be developed.

A network, such as an external communications network, may be compatiblewith now known, and/or to be developed, past, present, or futureversions of any, but not limited to the following network protocolstacks: ARCNET, AppleTalk, ATM, Bluetooth, DECnet, Ethernet, FDDI, FrameRelay, HIPPI, IEEE 1394, IEEE 802.11, IEEE-488, Internet Protocol Suite,IPX, Myrinet, OSI Protocol Suite, QsNet, RS-232, SPX, System NetworkArchitecture, Token Ring, USB, or X.25. A network may employ, forexample, TCP/IP, UDP, DECnet, NetBEUI, IPX, Appletalk, other, or thelike. Versions of the Internet Protocol (IP) may include IPv4, IPv6,other, and/or the like.

In the preceding detailed description, numerous specific details havebeen set forth to provide a thorough understanding of claimed subjectmatter. However, it will be understood by those skilled in the art thatclaimed subject matter may be practiced without these specific details.In other instances, methods and/or apparatuses that would be known byone of ordinary skill have not been described in detail so as not toobscure claimed subject matter. Some portions of the preceding detaileddescription have been presented in terms of logic, algorithms, and/orsymbolic representations of operations on binary signals and/or states,such as stored within a memory of a specific apparatus or specialpurpose computing device or a computing platform. In the context of thisparticular specification, the term specific apparatus or the likeincludes a general purpose computing device, such as general purposecomputer, once it is programmed to perform particular functions pursuantto instructions from program software.

Algorithmic descriptions and/or symbolic representations are examples oftechniques used by those of ordinary skill in the signal processingand/or related arts to convey the substance of their work to othersskilled in the art. An algorithm is here, and generally, is consideredto be a self-consistent sequence of operations and/or similar signalprocessing leading to a desired result. In this context, operationsand/or processing involve physical manipulation of physical quantities.Typically, although not necessarily, such quantities may take the formof electrical and/or magnetic signals and/or states capable of beingstored, transferred, combined, compared, processed or otherwisemanipulated as electronic signals and/or states representinginformation. It has proven convenient at times, principally for reasonsof common usage, to refer to such signals and/or states as bits, data,values, elements, symbols, characters, terms, numbers, numerals,information, and/or the like. It should be understood, however, that allof these and/or similar terms are to be associated with appropriatephysical quantities and are merely convenient labels. Unlessspecifically stated otherwise, as apparent from the followingdiscussion, it is appreciated that throughout this specificationdiscussions utilizing terms such as “processing,” “computing,”“calculating,” “determining”, “establishing”, “obtaining”,“identifying”, “selecting”, “generating”, and/or the like may refer toactions and/or processes of a specific apparatus, such as a specialpurpose computer and/or a similar special purpose computing device. Inthe context of this specification, therefore, a special purpose computerand/or a similar special purpose computing device is capable ofprocessing, manipulating and/or transforming signals and/or states,typically represented as physical electronic and/or magnetic quantitieswithin memories, registers, and/or other information storage devices,transmission devices, and/or display devices of the special purposecomputer and/or similar special purpose computing device. In the contextof this particular patent application, as mentioned, the term “specificapparatus” may include a general purpose computing device, such as ageneral purpose computer, once it is programmed to perform particularfunctions pursuant to instructions from program software.

In some circumstances, operation of a memory device, such as a change instate from a binary one to a binary zero or vice-versa, for example, maycomprise a transformation, such as a physical transformation. Withparticular types of memory devices, such a physical transformation maycomprise a physical transformation of an article to a different state orthing. For example, but without limitation, for some types of memorydevices, a change in state may involve an accumulation and/or storage ofcharge or a release of stored charge. Likewise, in other memory devices,a change of state may comprise a physical change, such as atransformation in magnetic orientation and/or a physical change ortransformation in molecular structure, such as from crystalline toamorphous or vice-versa. In still other memory devices, a change inphysical state may involve quantum mechanical phenomena, such as,superposition, entanglement, and/or the like, which may involve quantumbits (qubits), for example. The foregoing is not intended to be anexhaustive list of all examples in which a change in state form a binaryone to a binary zero or vice-versa in a memory device may comprise atransformation, such as a physical transformation. Rather, the foregoingis intended as illustrative examples.

While there has been illustrated and/or described what are presentlyconsidered to be example features, it will be understood by thoseskilled in the relevant art that various other modifications may be madeand/or equivalents may be substituted, without departing from claimedsubject matter. Additionally, many modifications may be made to adapt aparticular situation to the teachings of claimed subject matter withoutdeparting from one or more central concept(s) described herein.Therefore, it is intended that claimed subject matter not be limited tothe particular examples disclosed, but that such claimed subject mattermay also include all aspects falling within appended claims and/orequivalents thereof.

We claim:
 1. An apparatus comprising: a sensor interface to obtain oneor more output signal traces transmitted from a plurality of remotelylocated sensors; one or more processors configured to perform operationscomprising: sampling, at a configurable signal sampling interval, aplurality of signal sample values via the one or more output signaltraces from the plurality of remotely located sensors; storing, in atransmission array, an available portion of parameters extracted from afirst timeslot array and from a second timeslot array, wherein theavailable portion of parameters is extracted using an algorithmconfigured to assign specific parameters to the transmission array,wherein the algorithm is independent of the sampling; and transmitting,to an external communications network, an available portion of theparameters stored in the transmission array, at a transmission interval,wherein the transmission interval is independent of timeslot durationsof the first timeslot array and the second timeslot array, and whereinthe transmitting operation is performed independently of the storingoperation of the available portion of parameters.